What role do surfaces play in GB models? A new-generation of surface-generalized born model based on a novel gaussian surface for biomolecules

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach

BACKGROUND

Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth.

OBJECTIVES/AIMS

This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model.

METHODS

This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP.

RESULTS

The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis.

CONCLUSION

Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

An Unfitted Finite Element Poisson-Boltzmann Solver with Automatic Resolving of Curved Molecular Surface

So far, the existing Poisson-Boltzmann (PB) solvers that accurately take into account the interface jump conditions need a pregenerated body-fitted mesh (molecular surface mesh). However, qualified biomolecular surface meshing and its implementation into numerical methods remains a challenging and laborious issue, which practically hinders the progress of further developments and applications of a bunch of numerical methods in this field. In addition, even with a molecular surface mesh, it is only a low-order approximation of the original curved surface. In this article, an interface-penalty finite element method (IPFEM), which is a typical unfitted finite element method, is proposed to solve the Poisson-Boltzmann equation (PBE) without requiring the user to generate a molecular surface mesh. The Gaussian molecular surface is used to represent the molecular surface and can be automatically resolved with a high-order approximation within our method. Theoretical convergence rates of the IPFEM for the linear PB equation have been provided and are well validated on a benchmark problem with an analytical solution (we also noticed from numerical examples that the IPFEM has similar convergence rates for the nonlinear PBE). Numerical results on a set of different-sized biomolecules demonstrate that the IPFEM is numerically stable and accurate in the calculation of biomolecular electrostatic solvation energy.

Novel Inhibitors to MmpL3 Transporter of Mycobacterium tuberculosis by Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulations

Tuberculosis (TB)-causing bacterium Mycobacterium tuberculosis (Mtb) utilizes mycolic acids for building the mycobacterial cell wall, which is critical in providing defense against external factors and resisting antibiotic action. MmpL3 is a secondary resistance nodulation division transporter that facilitates the coupled transport of mycolic acid precursor into the periplasm using the proton motive force, thus making it an attractive drug target for TB infection. In 2019, X-ray crystal structures of MmpL3 from M. smegmatis were solved with a promising inhibitor SQ109, which showed promise against drug-resistant TB in Phase II clinical trials. Still, there is a pressing need to discover more effective MmpL3 inhibitors to counteract rising antibiotic resistance. In this study, structure-based high-throughput virtual screening combined with molecular dynamics (MD) simulations identified potential novel MmpL3 inhibitors. Approximately 17 million compounds from the ZINC15 database were screened against the SQ109 binding site on the MmpL3 protein using drug property filters and glide XP docking scores. From this, the top nine compounds and the MmpL3-SQ109 crystal complex structure each underwent 2 × 200 ns MD simulations to probe the inhibitor binding energetics to MmpL3. Four of the nine compounds exhibited stable binding properties and favorable drug properties, suggesting these four compounds could be potential novel inhibitors of MmpL3 for M. tuberculosis.

Unveiling the potential of recently FDA-approved drugs as quorum sensing inhibitors against P. Aeruginosa using high-performance computational techniques

Through cell-to-cell communication, activation of efflux pumps, formation of biofilms, and other mechanisms, pseudomonas aeruginosa's quorum sensing systems (QSS), notably the lasl/las-r system, contribute a vital role in the development of anti-microbial resistance (AMR). Identifying potential drugs against these targets could have significant implications for combating pseudomonal infections. The current study aims to identify promising recently FDA-approved drugs against lasl/las-r proteins. The ligands were selected from the FDA-approved drug lists of the last 5 years. Out of 202, 78 drugs were checked for interaction with lasl/las-r protein and 4 drugs revealed top binding conformations characterized by favorable energetic profiles within the active site of the las-r protein which were further assigned for 250-ns molecular dynamics (MD) simulation. The MD analysis confirmed the dynamical stability of brexanolone and oteseconazole with las-r protein. The root mean square deviation (RMSD), radius of gyration (Rg) and solvent-accessible surface area (SASA) analysis have indicated less deviation, more compactness of protein and less exposure of protein ligand complex to its surroundings as compared to the reference ligand-protein complex. The hydroxyl group in the oteseconazole whereas hydroxyl and ketone group in the brexanolone were responsible for hydrogen bonds with the active site residue of las r ptotein as indicated by ligand-protein contacts diagram. The binding energies per residue analysis revealed TYR-47 as the most contributing amino acid residue for interaction with oteseconazole and brexanolone. The identified drugs may be potential repurposing candidates against pseudomonal infections through inhibition of las-r protein.Communicated by Ramaswamy H. Sarma.

Probing the Activation Mechanisms of Agonist DPI-287 to Delta-Opioid Receptor and Novel Agonists Using Ensemble-Based Virtual Screening with Molecular Dynamics Simulations

Pain drugs targeting mu-opioid receptors face major addiction problems that have caused an epidemic. The delta-opioid receptor (DOR) has shown to not cause addictive effects when bound to an agonist. While the active conformation of the DOR in complex with agonist DPI-287 has been recently solved, there are still no FDA-approved agonists targeting it, providing the opportunity for structure-based virtual screening. In this study, the conformational plasticity of the DOR was probed using molecular dynamics (MD) simulations, identifying two representative conformations from clustering analysis. The two MD conformations as well as the crystal conformation of DOR were used to screen novel compounds from the ZINC database (17 million compounds), in which 69 drugs were picked as potential compounds based on their docking scores. Notably, 37 out of the 69 compounds were obtained from the simulated conformations. The binding stability of the 69 compounds was further investigated using MD simulations. Based on the MM-GBSA binding energy and the predicted drug properties, eight compounds were chosen as the most favorable, six of which were from the simulated conformations. Using a dynamic network model, the communication between the crystal agonist and the top eight molecules with the receptor was analyzed to confirm if these novel compounds share a similar activation mechanism to the crystal ligand. Encouragingly, docking of these eight compounds to the other two opioid receptors (kappa and mu) suggests their good selectivity toward DOR.

A comparative study of different docking methodologies to assess the protein-ligand interaction for the E. coli MurB enzyme

We have investigated the active site of E. coli MurB using the Quantum Mechanics/Molecular Mechanics (QM/MM) methodology. The docking of three novel series of 4-thiazolidinone derivatives has been performed using two methods: rigid docking and flexible docking (Induced Fit Docking: IFD). The results have been compared to understand the conformational aspects of the enzyme. The docking results from rigid docking show that the ligands with highly negative ΔG_bind have poor docking scores. In addition, the value of the regression coefficient (R) obtained on correlating the ΔG_bind and the experimental pMIC values is insignificant. On keeping the protein flexible, there is a remarkable improvement in both the docking score and ΔG_bind, along with a good value of R (0.64). Two important residues, Tyr254 and Try190 are found to be highly displaced during the flexible docking and hence their role in effective ligand binding has been confirmed. Thus, comparing the two methodologies, IFD has emerged as the more appropriate one for studying the E. coli MurB enzyme. To further substantiate the findings, MD studies over a time period of 20 ns have been performed on the IFD-LIII j and Rigid/XP-LIII j complexes and the results shows the former complex to be more stable, with lower average RMSD and higher average ΔG_bind.Communicated by Ramaswamy H. Sarma.

Computational and functional studies of the PI(4,5)P2 binding site of the TRPM3 ion channel reveal interactions with other regulators

Transient Receptor Potential Melastatin 3 (TRPM3) is a heat-activated ion channel expressed in peripheral sensory neurons and the central nervous system. TRPM3 activity depends on the membrane phospholipid phosphatidylinositol 4,5-bisphosphate [PI(4,5)P₂], but the molecular mechanism of activation by PI(4,5)P₂ is not known. As no experimental structure of TRPM3 is available, we built a homology model of the channel in complex with PI(4,5)P₂via molecular modeling. We identified putative contact residues for PI(4,5)P₂ in the pre-S1 segment, the S4-S5 linker, and the proximal C-terminal TRP-domain. Mutating these residues increased sensitivity to inhibition of TRPM3 by decreasing PI(4,5)P₂ levels. Changes in ligand-binding affinities via Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) showed reduced PI(4,5)P₂ affinity for the mutants. Mutating PI(4,5)P₂ interacting residues also reduced sensitivity for activation by the endogenous ligand pregnenolone sulfate (PregS), pointing to an allosteric interaction between PI(4,5)P₂ and PregS. Similarly, mutating residues in the PI(4,5)P₂ binding site in TRPM8 resulted in increased sensitivity to PI(4,5)P₂ depletion, and reduced sensitivity to menthol. Mutations of most PI(4,5)P₂-interacting residues in TRPM3 also increased sensitivity to inhibition by Gβγ, indicating allosteric interaction between Gβγ and PI(4,5)P₂. Disease-associated gain-of-function TRPM3 mutations on the other hand, resulted in no change of PI(4,5)P₂ sensitivity, indicating that mutations did not increase channel activity via increasing PI(4,5)P₂ interactions. Our data provide insight into the mechanism of regulation of TRPM3 by PI(4,5)P₂, its relationship to endogenous activators and inhibitors, as well as identify similarities and differences between PI(4,5)P₂ regulation of TRPM3 and TRPM8.

Identification of Novel Natural Product Inhibitors against Matrix Metalloproteinase 9 Using Quantum Mechanical Fragment Molecular Orbital-Based Virtual Screening Methods

Matrix metalloproteinases (MMPs) are calcium-dependent zinc-containing endopeptidases involved in multiple cellular processes. Among the MMP isoforms, MMP-9 regulates cancer invasion, rheumatoid arthritis, and osteoarthritis by degrading extracellular matrix proteins present in the tumor microenvironment and cartilage and promoting angiogenesis. Here, we identified two potent natural product inhibitors of the non-catalytic hemopexin domain of MMP-9 using a novel quantum mechanical fragment molecular orbital (FMO)-based virtual screening workflow. The workflow integrates qualitative pharmacophore modeling, quantitative binding affinity prediction, and a raw material search of natural product inhibitors with the BMDMS-NP library. In binding affinity prediction, we made a scoring function with the FMO method and applied the function to two protein targets (acetylcholinesterase and fibroblast growth factor 1 receptor) from DUD-E benchmark sets. In the two targets, the FMO method outperformed the Glide docking score and MM/PBSA methods. By applying this workflow to MMP-9, we proposed two potent natural product inhibitors (laetanine 9 and genkwanin 10) that interact with hotspot residues of the hemopexin domain of MMP-9. Laetanine 9 and genkwanin 10 bind to MMP-9 with a dissociation constant (K_D) of 21.6 and 0.614 μM, respectively. Overall, we present laetanine 9 and genkwanin 10 for MMP-9 and demonstrate that the novel FMO-based workflow with a quantum mechanical approach is promising to discover potent natural product inhibitors of MMP-9, satisfying the pharmacophore model and good binding affinity.

Towards optimal boundary integral formulations of the Poisson-Boltzmann equation for molecular electrostatics

The Poisson-Boltzmann equation offers an efficient way to study electrostatics in molecular settings. Its numerical solution with the boundary element method is widely used, as the complicated molecular surface is accurately represented by the mesh, and the point charges are accounted for explicitly. In fact, there are several well-known boundary integral formulations available in the literature. This work presents a generalized expression of the boundary integral representation of the implicit solvent model, giving rise to new forms to compute the electrostatic potential. Moreover, it proposes a strategy to build efficient preconditioners for any of the resulting systems, improving the convergence of the linear solver. We perform systematic benchmarking of a set of formulations and preconditioners, focusing on the time to solution, matrix conditioning, and eigenvalue spectrum. We see that the eigenvalue clustering is a good indicator of the matrix conditioning, and show that they can be easily manipulated by scaling the preconditioner. Our results suggest that the optimal choice is problem-size dependent, where a simpler direct formulation is the fastest for small molecules, but more involved second-kind equations are better for larger problems. We also present a fast Calderón preconditioner for first-kind formulations, which shows promising behavior for future analysis. This work sets the basis towards choosing the most convenient boundary integral formulation of the Poisson-Boltzmann equation for a given problem.

Novel inhibitors to ADP ribose phosphatase of SARS-CoV-2 identified by structure-based high throughput virtual screening and molecular dynamics simulations

The outbreak of a new coronavirus (SARS-CoV-2) was first identified in Wuhan, People's Republic of China, in 2019, which has led to a severe, life-threatening form of pneumonia (COVID-19). Research scientists all around the world have been trying to find small molecule drugs to treat COVID-19. In the present study, a conserved macrodomain, ADP Ribose phosphatase (ADRP), of a critical non-structural protein (Nsp3) in all coronaviruses was probed using large-scale Molecular Dynamics (MD) simulations to identify novel inhibitors. In our virtual screening workflow, the recently-solved X-ray complex structure, 6W6Y, with a substrate-mimics was used to screen 17 million ZINC15 compounds using drug property filters and Glide docking scores. The top twenty output compounds each underwent 200 ns MD simulations (i.e. 20 × 200 ns) to validate their individual stability as potential inhibitors. Eight out of the twenty compounds showed stable binding modes in the MD simulations, as well as favorable drug properties from our predctions. Therefore, our computational data suggest that the resulting top eight out of twenty compounds could potentially be novel inhibitors to ADRP of SARS-CoV-2.

Non-Carbohydrate Glycomimetics as Inhibitors of Calcium(II)-Binding Lectins

Because of the antimicrobial resistance crisis, lectins are considered novel drug targets. Pseudomonas aeruginosa utilizes LecA and LecB in the infection process. Inhibition of both lectins with carbohydrate-derived molecules can reduce biofilm formation to restore antimicrobial susceptibility. Here, we focused on non-carbohydrate inhibitors for LecA to explore new avenues for lectin inhibition. From a screening cascade we obtained one experimentally confirmed hit, a catechol, belonging to the well-known PAINS compounds. Rigorous analyses validated electron-deficient catechols as millimolar LecA inhibitors. The first co-crystal structure of a non-carbohydrate inhibitor in complex with a bacterial lectin clearly demonstrates the catechol mimicking the binding of natural glycosides with LecA. Importantly, catechol 3 is the first non-carbohydrate lectin ligand that binds bacterial and mammalian calcium(II)-binding lectins, giving rise to this fundamentally new class of glycomimetics.

Molecular Sparse Representation by a 3D Ellipsoid Radial Basis Function Neural Network via L1 Regularization

The three-dimensional structures and shapes of biomolecules provide essential information about their interactions and functions. Unfortunately, the computational cost of biomolecular shape representation is an active challenge which increases rapidly as the number of atoms increase. Recent developments in sparse representation and deep learning have shown significant improvements in terms of time and space. A sparse representation of molecular shape is also useful in various other applications, such as molecular structure alignment, docking, and coarse-grained molecular modeling. We have developed an ellipsoid radial basis function neural network (ERBFNN) and an algorithm for sparsely representing molecular shape. To evaluate a sparse representation model of molecular shape, the Gaussian density map of the molecule is approximated using ERBFNN with a relatively small number of neurons. The deep learning models were trained by optimizing a nonlinear loss function with L1 regularization. Experimental results reveal that our algorithm can represent the original molecular shape with a relatively higher accuracy and fewer scale of ERBFNN. Our network in principle is applicable to the multiresolution sparse representation of molecular shape and coarse-grained molecular modeling. Executable files are available at https://github.com/SGUI-LSEC/SparseGaussianMolecule. The program was implemented in PyTorch and was run on Linux.

Inhibitory potential of repurposed drugs against the SARS-CoV-2 main protease: a computational-aided approach

The exponential increase in cases and mortality of coronavirus disease (COVID-19) has called for a need to develop drugs to treat this infection. Using in silico and molecular docking approaches, this study investigated the inhibitory effects of Pradimicin A, Lamivudine, Plerixafor and Lopinavir against SARS-CoV-2 M^pro. ADME/Tox of the ligands, pharmacophore hypothesis of the co-crystalized ligand and the receptor, and docking studies were carried out on different modules of Schrodinger (2019-4) Maestro v12.2. Among the ligands subjected to ADME/Tox by QikProp, Lamivudine demonstrated drug-like physico-chemical properties. A total of five pharmacophore binding sites (A3, A4, R9, R10, and R11) were predicted from the co-crystalized ligand and the binding cavity of the SARS-CoV-2 M^pro. The docking result showed that Lopinavir and Lamivudine bind with a higher affinity and lower free energy than the standard ligand having a glide score of -9.2 kcal/mol and -5.3 kcal/mol, respectively. Plerixafor and Pradimicin A have a glide score of -3.7 kcal/mol and -2.4 kcal/mol, respectively, which is lower than the co-crystallized ligand with a glide score of -5.3 kcal/mol. Molecular dynamics confirmed that the ligands maintained their interaction with the protein with lower RMSD fluctuations over the trajectory period of 100 nsecs and that GLU166 residue is pivotal for binding. On the whole, present study specifies the repurposing aptitude of these molecules as inhibitors of SARS-CoV-2 M^pro with higher binding scores and forms energetically stable complexes with M^pro.Communicated by Ramaswamy H. Sarma.

Deciphering the interaction of puerarin with cancer macromolecules: An in silico investigation

The worldwide expanding increment in cancer pervasiveness is disturbing and this disease ranks among the main causes of mortality in both developing and developed countries. Unfortunately, available treatment options come with serious side effects and do not guarantee complete success. Although numerous models have been proposed for the development of better therapeutic agent, however the exact mechanism are still poorly understood. This then calls for continued research aimed at developing new drugs as an alternative or adjuvant anticancer agents. Here we have identified five vital proteins (CDK-2, Bcl-2, CDK-6, VEGFR, and IGF-1R) that aid tumor growth and we inhibited the activity of these proteins with Puerarin. Puerarin is an isoflavonoid C-glycosides used as a therapeutic agent against various human ailments. Our findings revealed that Puerarin fulfilled Veber's rule. Added to this, CDK-6 and Bcl-2 had better glide scores for puerarin than the control (doxorubicin) and molecular simulation showed the stability of the complexes. These findings suggest that inhibiting CDK-6 and Bcl-2 with Puerarin could prove more effective in the management of cancer than doxorubicin. Overall, this study provides a new direction that could facilitate rational drug design for cancer.Communicated by Ramaswamy H. Sarma.

Computer-aided identification of lead compounds as Staphylococcal epidermidis FtsZ inhibitors using molecular docking, virtual screening, DFT analysis, and molecular dynamic simulation

In an effort to face the multiple drug-resistant bacteria, various approaches have been discovered to design potent compounds and search new targets through computational design tools. With an aim to identify selective inhibitors against filamentous temperature-sensitive mutant Z (FtsZ), a library of Phase database compounds have been virtually screened. High-throughput virtual screening of compounds against Staphylococcal epidermidis FtsZ protein (4M8I) was performed using three sequential docking modes like high-throughput virtual screening, Glide standard precision, followed by Glide extra precision. Four top-ranked compounds were selected from molecular mechanics-generalized Born surface area (MM-GBSA) binding energy with better predicted free binding energies of - 89.309, - 54.382, - 53.667, and - 52.133 kcal/mol, respectively. It is also showed that the contribution of van der Waals and electrostatic solvation energy terms are playing a major part to make the hit molecule (T6288784) binding to S. epidermidis FtsZ protein. The result of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and energy gap analysis predicts the molecular reactivity and stability of hit molecules. Subsequently, Lipinski's rule of five and properties of absorption, distribution, metabolism, and excretion (ADME) were to calculate their bioavailability. The average binding energy - 9.67 kcal/mol of the best proposed hit molecule (T6288784) was found with half-maximal inhibitory concentration (IC50) value to be 75.53 nM. A 15-ns molecular dynamics simulation study revealed the stable conformation of hit molecule. On a wide-range research discipline, in silico studies of our proposed compound confirm promising results and can be successfully used towards the development of novel FtsZ inhibitor with better binding affinity. Graphical Abstract.

Multiple 3D-QSAR modeling, e-pharmacophore, molecular docking, and in vitro study to explore novel AChE inhibitors

Ligand-based and energy-optimized structure-based approaches were considered to obtain excellent candidates as AChE inhibitors. The known AChE inhibitors were utilized to develop a pharmacophore hypothesis, HPRRR and X-ray crystallographic structures of AChE were used to produce three e-pharmacophore hypotheses viz. AHHRR, AHRR, and DHRR. Based on in silico approaches, we came across eight structurally diverse hits as non-competitive AChE inhibitors with good ADME properties. The best four hits, ZINC20592007, ZINC05354646, ZINC20649934, and ZINC39154782 were non-toxic, neuroprotective, and were selective AChE inhibitors (IC50 values 482 ± 1.88 nM, 580 ± 1.63 nM, 854 ± 2.65 nM, and 636 ± 1.79 nM respectively). The hits showed non-competitive inhibition of AChE at PAS site with attractive K i values (0.21 ± 0.027 μM, 0.27 ± 0.064 μM, 0.3 ± 0.018 μM, and 0.28 ± 0.032 μM for ZINC20592007, ZINC05354646, ZINC20649934, and ZINC39154782 respectively), and increased the cholinergic activity as well as inhibited Aβ aggregation.

Potent Inhibition of Hepatitis E Virus Release by a Cyclic Peptide Inhibitor of the Interaction between Viral Open Reading Frame 3 Protein and Host Tumor Susceptibility Gene 101

Hepatitis E virus (HEV) generally causes self-limiting acute viral hepatitis in normal individuals. It causes a more severe disease in immunocompromised persons and pregnant women. Due to the lack of an efficient cell culture system or animal model, the life cycle of the virus is understudied, few antiviral targets are known, and very few antiviral candidates against HEV infection have been identified. Inhibition of virus release is one possible antiviral development strategy, which limits the spread of the virus. Previous studies have demonstrated the essential role of the interaction between the PSAP motif of the viral open reading frame 3 protein (ORF3-PSAP) and the UEV domain of the host tumor susceptibility gene 101 (TSG101) protein (UEV-TSG101) in mediating the release of genotype 3 HEV. Cyclic peptide (CP) inhibitors of the interaction between the human immunodeficiency virus (HIV) gag-PTAP motif and UEV-TSG101 are known to block the release of HIV. Using a molecular dynamic simulation, we observed that both gag-PTAP and ORF3-PSAP motifs bind to the same site in UEV-TSG101 by hydrogen bonding. HIV-released inhibitory CPs also displayed binding to the same site in UEV-TSG101, indicating that they may compete with ORF3-PSAP or gag-PTAP for binding to UEV-TSG101. Two independent assays confirmed the ability of a cyclic peptide (CP11) to inhibit the ORF3-TSG101 interaction. CP11 treatment also reduced the release of both genotype 1 and genotype 3 HEV by approximately 90%, with a 50% inhibitory concentration (IC₅₀) of 2 μM. Thus, CP11 appears to be an attractive candidate for further validation of its anti-HEV properties.IMPORTANCE There is no specific therapy against hepatitis E virus (HEV)-induced hepatic and nonhepatic health problems. Prevention of the release of the progeny viruses from infected cells is an attractive strategy to limit the spread of the virus. Interactions between the viral open reading frame 3 and the host tumor susceptibility gene 101 proteins have been shown to be essential for the release of genotype 3 HEV from infected cells. In this study, we have identified a cyclic peptide inhibitor of the above-mentioned interaction and demonstrate the efficiency of the inhibitor in preventing virus release from infected cells. Thus, our findings uncover the possibility of developing a specific antiviral agent against HEV by blocking its release from infected cells.

Frontiers in biomolecular mesh generation and molecular visualization systems

With the development of biomolecular modeling and simulation, especially implicit solvent modeling, higher requirements are set for the stability, efficiency and mesh quality of molecular mesh generation software. In this review, we summarize the recent works in biomolecular mesh generation and molecular visualization. First, we introduce various definitions of molecular surface and corresponding meshing software. Second, as the mesh quality significantly influences biomolecular simulation, we investigate some remeshing methods in the fields of computer graphics and molecular modeling. Then, we show the application of biomolecular mesh in the boundary element method (BEM) and the finite element method (FEM). Finally, to conveniently visualize the numerical results based on the mesh, we present two types of molecular visualization systems.

Pharmacophore generation, atom-based 3D-QSAR, molecular docking and molecular dynamics simulation studies on benzamide analogues as FtsZ inhibitors

FtsZ is an appealing target for the design of antimicrobial agent that can be used to defeat the multidrug-resistant bacterial pathogens. Pharmacophore modelling, molecular docking and molecular dynamics (MD) simulation studies were performed on a series of three-substituted benzamide derivatives. In the present study a five-featured pharmacophore model with one hydrogen bond acceptors, one hydrogen bond donors, one hydrophobic and two aromatic rings was developed using 97 molecules having MIC values ranging from .07 to 957 μM. A statistically significant 3D-QSAR model was obtained using this pharmacophore hypothesis with a good correlation coefficient (R² = .8319), cross validated coefficient (Q² = .6213) and a high Fisher ratio (F = 103.9) with three component PLS factor. A good correlation between experimental and predicted activity of the training (R² = .83) and test set (R² = .67) molecules were displayed by ADHRR.1682 model. The generated model was further validated by enrichment studies using the decoy test and MAE-based criteria to measure the efficiency of the model. The docking studies of all selected inhibitors in the active site of FtsZ protein showed crucial hydrogen bond interactions with Val 207, Asn 263, Leu 209, Gly 205 and Asn-299 residues. The binding free energies of these inhibitors were calculated by the molecular mechanics/generalized born surface area VSGB 2.0 method. Finally, a 15 ns MD simulation was done to confirm the stability of the 4DXD-ligand complex. On a wider scope, the prospect of present work provides insight in designing molecules with better selective FtsZ inhibitory potential.

Can human allergy drug fexofenadine, an antagonist of histamine (H1) receptor, be used to treat dog and cat? Homology modeling, docking and molecular dynamic Simulation of three H1 receptors in complex with fexofenadine

Fexofenadine, a potent antagonist to human histamine 1 (H₁) receptor, is a non-sedative third generation antihistamine that is widely used to treat various human allergic conditions such as allergic rhinitis, conjunctivitis and atopic dermatitis. Encouragingly, it's been successfully used to treat canine atopic dermatitis, this supports the notion that it might have a great potential for treating other canine allergic conditions and other mammal pets such as dog. Regrettably, while there is a myriad of studies conducted on the interactions of antihistamines with human H₁ receptor, the similar studies on non-human pet H₁ are considerably scarce. The published studies using the first and second generation antihistamines drugs have shown that the antihistamine response is varied and unpredictable. Thus, to probe its efficacy on pet, the homology models of dog and cat H₁ receptors were built based on the crystal structure of human H₁ receptor bound to antagonist doxepin (PDB 3RZE) and fexofenadine was subsequently docked to human, dog and cat H₁ receptors. The docked complexes are then subjected to 1000ns molecular dynamics (MD) simulations with explicit membrane. Our calculated MM/GBSA binding energies indicated that fexofenadine binds comparably to the three receptors; and our MD data also showed the binding poses, structural and dynamic features among three receptors are very similar. Therefore, our data supported the application of fexofenadine to the H₁ related allergic conditions of dog and cat. Nonetheless, subtle systemic differences among human, dog and cat H₁ receptors were also identified. Clearly, there is still a space to develop a more selective, potent and safe antihistamine alternatives such as Fexofenadine for dog or cat based on these differences. Our computation approach might provide a fast and economic way to predict if human antihistamine drugs can also be safely and efficaciously administered to animals.

Structural and molecular analysis of a protective epitope of Lyme disease antigen OspA and antibody interactions

The murine monoclonal antibody LA-2 recognizes a clinically protective epitope on outer surface protein (OspA) of Borrelia burgdorferi, the causative agent of Lyme disease in North America. Human antibody equivalence to LA-2 is the best serologic correlate of protective antibody responses following OspA vaccination. Understanding the structural and functional basis of the LA-2 protective epitope is important for developing OspA-based vaccines and discovering prophylactic antibodies against Lyme disease. Here, we present a detailed structure-based analysis of the LA-2/OspA interaction interface and identification of residues mediating antibody recognition. Mutations were introduced into both OspA and LA-2 on the basis of computational predictions on the crystal structure of the complex and experimentally tested for in vitro binding and borreliacidal activity. We find that Y32 and H49 on the LA-2 light chain, N52 on the LA-2 heavy chain and residues A208, N228 and N251 on OspA were the key constituents of OspA/LA-2 interface. These results reveal specific residues that may be exploited to modulate recognition of the protective epitope of OspA and have implications for developing prophylactic passive antibodies.

Computational approaches to predict binding interactions between mammalian tyrosinases and (S)-(+)-decursin and its analogues as potent inhibitors

Using computational methods, we elucidated the binding modes of (S)-(+)-decursin with mouse and human tyrosinases, and thereby provided insight to new tyrosinase inhibitors.

Effective Approximation of Molecular Volume Using Atom-Centered Dielectric Functions in Generalized Born Models

The generalized Born (GB) theory is a prime choice for implicit treatment of solvent that provides a favorable balance between efficiency and accuracy for reliable simulation of protein conformational equilibria. In GB, the dielectric boundary is a key physical property that needs to be properly described. While it is widely accepted that the molecular surface (MS) should provide the most physical description, most existing GB models are based on van der Waals (vdW)-like surfaces for computational simplicity and efficiency. A simple and effective approximation to molecular volume is explored here using atom-centered dielectric functions within the context of a generalized Born model with simple switching (GBSW). The new model, termed GBSW/MS2, is as efficient as the original vdW-like-surface-based GBSW model, but is able to reproduce the Born radii calculated from the "exact" Poisson-Boltzmann theory with a correlation of 0.95. More importantly, examination of the potentials of mean force of hydrogen-bonding and charge-charge interactions demonstrates that GBSW/MS2 correctly captures the first desolvation peaks, a key signature of true MS. Physical parameters including atomic input radii and peptide backbone torsion were subsequently optimized on the basis of solvation free energies of model compounds, potentials of mean force of their interactions, and conformational equilibria of a set of helical and β-hairpin model peptides. The resulting GBSW/MS2 protein force field reasonably recapitulates the structures and stabilities of these model peptides. Several remaining limitations and possible future developments are also discussed.

Pharmacophore model prediction, 3D-QSAR and molecular docking studies on vinyl sulfones targeting Nrf2-mediated gene transcription intended for anti-Parkinson drug design

Despite intense research efforts towards clinical and molecular causes of Parkinson disease (PD), the etiology of disease still remains unclear. However, recent studies have provided ample evidences that the oxidative stress is the key player that contributes a lot to dopaminergic (DAergic) neurodegeneration in brain. It is due to the discrepancy of antioxidant defence system of which nuclear factor erythroid 2-related factor 2 (Nrf2) signalling is of central contour. In the current study, potent heme oxygenase-1 agonists (Nrf2 signalling regulator), vinyl sulfones, were selected and an optimal pharmacophore model was brought forth which was examined using a decoy set by atom-based 3D-QSAR. The best four-feature model consists of two hydrogen bond acceptors and two aromatic rings, which has the highest correlation coefficient, R(2) = .71 and [Formula: see text] = .73 in QSAR. These ligands were further studied for molecular docking with Nrf2-keap protein to gain insight into the major binding motifs followed by analysing pharmacokinetic properties to evaluate their bioavailability dominance. From this study, it is concluded that vinyl sulfones could be ideal compounds for targeting Nrf2 pathway which in turn halt the PD progression. Hence, these can be considered as potential leads for drug development against the same.

Parameterization for molecular Gaussian surface and a comparison study of surface mesh generation

The molecular Gaussian surface has been frequently used in the field of molecular modeling and simulation. Typically, the Gaussian surface is defined using two controlling parameters; the decay rate and isovalue. Currently, there is a lack of studies in which a systematic approach in the determination of optimal parameterization according to the geometric features has been done. In this paper, surface area, volume enclosed by the surface and Hausdorff distance are used as three criteria for the parameterization to make the Gaussian surface approximate the solvent excluded surface (SES) well. For each of these three criteria, a search of the parameter space is carried out in order to determine the optimal parameter values. The resulted parameters are close to each other and result in similar calculated molecular properties. Approximation of the VDW surface is also done by analyzing the explicit expressions of the Gaussian surface and VDW surface, which analysis and parameters can be similarly applied to the solvent accessible surface (SAS) due to its geometric similarity to the VDW surface. Once the optimal parameters are obtained, we compare the performance of our Gaussian surface generation software TMSmesh with other commonly used software programs, focusing primarily on mesh quality and fidelity. Additionally, the Poisson-Boltzmann solvation energies based on the surface meshes generated by TMSmesh and those generated by other software programs are calculated and compared for a set of molecules with different sizes. The results of these comparisons validate both the accuracy and the applicability of the parameterized Gaussian surface.

Mathematical and computational modeling in biology at multiple scales

A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined.

Physics-based enzyme design: predicting binding affinity and catalytic activity

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications.

Exploring the free-energy landscape of carbohydrate-protein complexes: development and validation of scoring functions considering the binding-site topology

Carbohydrates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the structure-based design of carbohydrate-based ligands. We assembled a diverse data set comprising 273 carbohydrate-protein crystal structures with known binding affinity and evaluated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as combinations thereof. Unfortunately, the tested functions were not capable of reproducing binding affinities in the studied complexes. To simplify the complex free-energy surface of carbohydrate-protein systems, we classified the studied proteins according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classification scheme reproduced binding affinities in the carbohydrate data set with an r(2) of 0.71 and root-mean-squared-error of 1.25 kcal/mol (N = 236). The improvement in model performance underlines the significance of the differences in the local micro-environments of carbohydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and solvent exposure.

A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring

Enzyme design is an important area of ongoing research with a broad range of applications in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we develop a computational protocol using an approach that combines molecular dynamics, docking, and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focuses are to understand the molecular basis of substrate recognition and binding in an S-stereoselective ω-aminotransferase (ω-AT), which naturally catalyzes the transamination of pyruvate into alanine, and to predict mutations that enhance the catalytic efficiency of the enzyme. The conversion of (R)-ethyl 5-methyl-3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate in the context of several ω-AT mutants was evaluated using the computational protocol developed in this work. We correctly identify the mutations that yield the greatest improvements in enzyme activity (20-60-fold improvement over wild type) and confirm that the computationally predicted structure of a highly active mutant reproduces key structural aspects of the variant, including side chain conformational changes, as determined by X-ray crystallography. Overall, the protocol developed here yields encouraging results and suggests that computational approaches can aid in the redesign of enzymes with improved catalytic efficiency.

Accuracy of continuum electrostatic calculations based on three common dielectric boundary definitions

We investigate the influence of three common definitions of the solute/solvent dielectric boundary (DB) on the accuracy of the electrostatic solvation energy ΔG_el computed within the Poisson Boltzmann and the generalized Born models of implicit solvation. The test structures include small molecules, peptides and small proteins; explicit solvent ΔG_el are used as accuracy reference. For common atomic radii sets BONDI, PARSE (and ZAP9 for small molecules) the use of van der Waals (vdW) DB results, on average, in considerably larger errors in ΔG_el than the molecular surface (MS) DB. The optimal probe radius ρ_w for which the MS DB yields the most accurate ΔG_el varies considerably between structure types. The solvent accessible surface (SAS) DB becomes optimal at ρ_w ~ 0.2 Å (exact value is sensitive to the structure and atomic radii), at which point the average accuracy of ΔG_el is comparable to that of the MS-based boundary. The geometric equivalence of SAS to vdW surface based on the same atomic radii uniformly increased by ρ_w gives the corresponding optimal vdW DB. For small molecules, the optimal vdW DB based on BONDI + 0.2 Å radii can yield ΔG_el estimates at least as accurate as those based on the optimal MS DB. Also, in small molecules, pairwise charge-charge interactions computed with the optimal vdW DB are virtually equal to those computed with the MS DB, suggesting that in this case the two boundaries are practically equivalent by the electrostatic energy criteria. In structures other than small molecules, the optimal vdW and MS dielectric boundaries are not equivalent: the respective pairwise electrostatic interactions in the presence of solvent can differ by up to 5 kcal/mol for individual atomic pairs in small proteins, even when the total ΔG_el are equal. For small proteins, the average decrease in pairwise electrostatic interactions resulting from the switch from optimal MS to optimal vdW DB definition can be mimicked within the MS DB definition by doubling of the solute dielectric constant. However, the use of the higher interior dielectric does not eliminate the large individual deviations between pairwise interactions computed within the two DB definitions. It is argued that while the MS based definition of the dielectric boundary is more physically correct in some types of practical calculations, the choice is not so clear in some other common scenarios.

Molecular modeling study of dihydrofolate reductase inhibitors. Molecular dynamics simulations, quantum mechanical calculations, and experimental corroboration

A molecular modeling study on dihydrofolate reductase (DHFR) inhibitors was carried out. By combining molecular dynamics simulations with semiempirical (PM6), ab initio, and density functional theory (DFT) calculations, a simple and generally applicable procedure to evaluate the binding energies of DHFR inhibitors interacting with the human enzyme is reported here, providing a clear picture of the binding interactions of these ligands from both structural and energetic viewpoints. A reduced model for the binding pocket was used. This approach allows us to perform more accurate quantum mechanical calculations as well as to obtain a detailed electronic analysis using the quantum theory of atoms in molecules (QTAIM) technique. Thus, molecular aspects of the binding interactions between inhibitors and the DHFR are discussed in detail. A significant correlation between binding energies obtained from DFT calculations and experimental IC₅₀ values was obtained, predicting with an acceptable qualitative accuracy the potential inhibitor effect of nonsynthesized compounds. Such correlation was experimentally corroborated synthesizing and testing two new inhibitors reported in this paper.

Influence of Grid Spacing in Poisson-Boltzmann Equation Binding Energy Estimation

Grid-based solvers of the Poisson-Boltzmann, PB, equation are routinely used to estimate electrostatic binding, ΔΔG_el, and solvation, ΔG_el, free energies. The accuracies of such estimates are subject to grid discretization errors from the finite difference approximation to the PB equation. Here, we show that the grid discretization errors in ΔΔG_el are more significant than those in ΔG_el, and can be divided into two parts: (i) errors associated with the relative positioning of the grid and (ii) systematic errors associated with grid spacing. The systematic error in particular is significant for methods, such as the molecular mechanics PB surface area, MM-PBSA, approach that predict electrostatic binding free energies by averaging over an ensemble of molecular conformations. Although averaging over multiple conformations can control for the error associated with grid placement, it will not eliminate the systematic error, which can only be controlled by reducing grid spacing. The present study indicates that the widely-used grid spacing of 0.5 Å produces unacceptable errors in ΔΔG_el, even though its predictions of ΔG_el are adequate for the cases considered here. Although both grid discretization errors generally increase with grid spacing, the relative sizes of these errors differ according to the solute-solvent dielectric boundary definition. The grid discretization errors are generally smaller on the Gaussian surface used in the present study than on either the solvent-excluded or van der Waals surfaces, which both contain more surface discontinuities (e.g., sharp edges and cusps). Additionally, all three molecular surfaces converge to very different estimates of ΔΔG_el.

Design, synthesis, and X-ray analysis of a glycoconjugate bound to Mycobacterium tuberculosis antigen 85C

Tuberculosis (TB) is a global health threat with nearly 500 000 new cases of multidrug-resistant TB estimated to occur every year, so new drugs are desperately needed. A number of current antimycobacterial drugs work by interfering with the biosynthesis of key components of the mycolylarabinogalactan (mAG). In light of this observation, other enzymes involved in the synthesis of the mAG should also serve as targets for antimycobacterial drug development. One potential target is the Antigen 85 (Ag85) complex, a family of mycolyltransferases that are responsible for the transfer of mycolic acids from trehalose monomycolate (TMM) to the arabinogalactan. Virtual thiophenyl-arabinoside conjugates were docked to antigen Ag85C (PDB code: 1va5 ) using Glide. Compounds with good docking scores were synthesized by a Gewald synthesis followed by linking to 5-thioarabinofuranosides. The resulting thiophenyl-thioarabinofuranosides were assayed for inhibition of mycoyltransferase activity using a 4-methylumbelliferyl butyrate fluorescence assay. The conjugates showed K(i) values ranging from 18.2 to 71.0 μM. The most potent inhibitor was soaked into crystals of Mycobacterium tuberculosis antigen 85C and the structure of the complex determined. The X-ray structure shows the compound bound within the active site of the enzyme with the thiophene moiety positioned in the putative α-chain binding site of TMM and the arabinofuranoside moiety within the known carbohydrate-binding site as exhibited for the Ag85B-trehalose crystal structure. Unexpectedly, no specific hydrogen bonding interactions are being formed between the arabinofuranoside and the carbohydrate-binding site of the active site suggesting that the binding of the arabinoside within this structure is driven by shape complementarily between the arabinosyl moiety and the carbohydrate binding site.

Discovery of diverse human dihydroorotate dehydrogenase inhibitors as immunosuppressive agents by structure-based virtual screening

This study applied an efficient virtual screening strategy integrating molecular docking with MM-GBSA rescoring to identify diverse human dihydroorotate dehydrogenase (hDHODH) inhibitors. Eighteen compounds with IC(50) values ranging from 0.11 to 18.8 μM were identified as novel hDHODH inhibitors that exhibited overall species-selectivity over Plasmodium falciparum dihydroorotate dehydrogenase (pfDHODH). Compound 8, the most potent one, showed low micromolar inhibitory activity against hDHODH with an IC(50) value of 0.11 μM. Moreover, lipopolysaccharide-induced B-cell assay and mixed lymphocyte reaction assay revealed that most of the hits showed potent antiproliferative activity against B and T cells, which demonstrates their potential application as immunosuppressive agents. In particular, compound 18 exhibited potent B-cell inhibitory activity (IC(50) = 1.78 μM) and presents a B-cell-specific profile with 17- and 26-fold selectivities toward T and Jurkat cells, respectively.

Bluues: a program for the analysis of the electrostatic properties of proteins based on generalized Born radii

BACKGROUND

The Poisson-Boltzmann (PB) equation and its linear approximation have been widely used to describe biomolecular electrostatics. Generalized Born (GB) models offer a convenient computational approximation for the more fundamental approach based on the Poisson-Boltzmann equation, and allows estimation of pairwise contributions to electrostatic effects in the molecular context.

RESULTS

We have implemented in a single program most common analyses of the electrostatic properties of proteins. The program first computes generalized Born radii, via a surface integral and then it uses generalized Born radii (using a finite radius test particle) to perform electrostaic analyses. In particular the ouput of the program entails, depending on user's requirement: 1) the generalized Born radius of each atom; 2) the electrostatic solvation free energy; 3) the electrostatic forces on each atom (currently in a developmental stage); 4) the pH-dependent properties (total charge and pH-dependent free energy of folding in the pH range -2 to 18; 5) the pKa of all ionizable groups; 6) the electrostatic potential at the surface of the molecule; 7) the electrostatic potential in a volume surrounding the molecule;

CONCLUSIONS

Although at the expense of limited flexibility the program provides most common analyses with requirement of a single input file in PQR format. The results obtained are comparable to those obtained using state-of-the-art Poisson-Boltzmann solvers. A Linux executable with example input and output files is provided as supplementary material.

The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling

A novel energy model (VSGB 2.0) for high resolution protein structure modeling is described, which features an optimized implicit solvent model as well as physics-based corrections for hydrogen bonding, π-π interactions, self-contact interactions, and hydrophobic interactions. Parameters of the VSGB 2.0 model were fit to a crystallographic database of 2239 single side chain and 100 11-13 residue loop predictions. Combined with an advanced method of sampling and a robust algorithm for protonation state assignment, the VSGB 2.0 model was validated by predicting 115 super long loops up to 20 residues. Despite the dramatically increasing difficulty in reconstructing longer loops, a high accuracy was achieved: all of the lowest energy conformations have global backbone RMSDs better than 2.0 Å from the native conformations. Average global backbone RMSDs of the predictions are 0.51, 0.63, 0.70, 0.62, 0.80, 1.41, and 1.59 Å for 14, 15, 16, 17, 18, 19, and 20 residue loop predictions, respectively. When these results are corrected for possible statistical bias as explained in the text, the average global backbone RMSDs are 0.61, 0.71, 0.86, 0.62, 1.06, 1.67, and 1.59 Å. Given the precision and robustness of the calculations, we believe that the VSGB 2.0 model is suitable to tackle "real" problems, such as biological function modeling and structure-based drug discovery.

A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series

We introduce the "Prime-ligand" method for ranking ligands in congeneric series. The method employs a single scoring function, the OPLS-AA/GBSA molecular mechanics/implicit solvent model, for all stages of sampling and scoring. We evaluate the method using 12 test sets of congeneric series for which experimental binding data is available in the literature, as well as the structure of one member of the series bound to the protein. Ligands are "docked" by superimposing a common stem fragment among the compounds in the series using a crystal complex from the Protein Data Bank and sampling the conformational space of the variable region. Our results show good correlation between our predicted rankings and the experimental data for cases in which binding affinities differ by at least 1 order of magnitude. For 11 out of 12 cases, >90% of such ligand pairs could be correctly ranked, while for the remaining case, Factor Xa, 76% of such pairs were correctly ranked. A small number of compounds could not be docked using the current protocol because of the large size of functional groups that could not be accommodated by a rigid receptor. CPU requirements for the method, involving CPU minutes per ligand, are modest compared with more rigorous methods that use similar force fields, such as free energy perturbation. We also benchmark the scoring function using series of ligands bound to the same protein within the CSAR data set. We demonstrate that energy minimization of ligands in the crystal structures is critical to obtain any correlation with experimentally determined binding affinities.